{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 拿去花活跃用户年化价值分布\n",
    "- 活跃用户：有订单的用户\n",
    "- 用户价值：按照订单金额0.3%，分期金额0.36%计算\n",
    "- 年化：将用户价值按激活时间按每年365.25天计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数据准备\n",
    "- 读取导出的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"b:\\\\user_value_export__20170918.txt\",sep='\\t',\n",
    "                  dtype={\"bu\":str,\"uid\": str,\"active_date\":str,\"bu_value\":float})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bu</th>\n",
       "      <th>uid</th>\n",
       "      <th>active_date</th>\n",
       "      <th>bu_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>度假</td>\n",
       "      <td>_CFT0100000017976546</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>度假</td>\n",
       "      <td>32080365</td>\n",
       "      <td>2016-12-25</td>\n",
       "      <td>78.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>度假</td>\n",
       "      <td>13923389200</td>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>66.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>度假</td>\n",
       "      <td>oldmoon</td>\n",
       "      <td>2016-12-17</td>\n",
       "      <td>164.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>度假</td>\n",
       "      <td>_WeChat436270669</td>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bu                   uid active_date  bu_value\n",
       "0  度假  _CFT0100000017976546  2016-12-25     12.26\n",
       "1  度假              32080365  2016-12-25     78.67\n",
       "2  度假           13923389200  2016-12-29     66.92\n",
       "3  度假               oldmoon  2016-12-17    164.39\n",
       "4  度假      _WeChat436270669  2016-11-15     19.15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_palette('bright', desat=.6)\n",
    "sns.set_context(rc={'figure.figsize': (10, 5) } )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 数据按BU分开，排除激活时间在最近三个月的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['log_value'] = df['bu_value'].apply(np.log10)\n",
    "train = df[(df[\"bu\"]=='火车票') & (df[\"bu_value\"] > 0) & (df['active_date'] < '2017-06-18')]\n",
    "flight = df[(df[\"bu\"]=='机票') & (df[\"bu_value\"] > 0) & (df['active_date'] < '2017-06-18')]\n",
    "hotel = df[(df[\"bu\"]=='酒店') & (df[\"bu_value\"] > 0) & (df['active_date'] < '2017-06-18')]\n",
    "vacation = df[(df[\"bu\"]=='度假') & (df[\"bu_value\"] > 0) & (df['active_date'] < '2017-06-18')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa9713c8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.distplot(vacation['bu_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 显示分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfab8160>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s3DMN1Bla08uawR5mZmBqpk5vT9W+p6fG5IEZDkzV6e+rMdhfnWD2T9WZnq629fdVz2vW\nv2qKnXumoA4zdRp/16lTfQ1Qq0GtVqN28OvqcX32CXWo1znY5qVcwFo75IvZxwd/dg9uP/yHud7q\nlQ7ZdOjVtD0DBxjdPfXiPhx66Bb/b1q9ftt+zNOHubT8P9vmpefqE0DfqgPV93aBfZvv36ddP5p/\nPppfq/7TJ7R4sRe/RLufg3q9/uKfud79PLfzwMGnzv68UoOeWvV3jepntj5T9WVm9hiNP83P7anV\nqDUe12o1pmfqHDhQZ7pep7+3+r80NV1n/4HqGAMDNfp7a0weqDO5f4aeWo1Vq2r09tTYNzHDvskZ\n+vtr1XuEdRgbnzn4Uaxr1/Swf3+d53dPMTE5w8Z1fWxY28sLu6d4auQAMzPw6uF+1q/p5XtPTvDd\nH05w9FFjnLC5Otbt9+zk/35nLye9ehXvPHcDW948dEQGlp2E/jnAHQCZ+UBEnNG07xRgR2aOAkTE\nvcC5wFlt2rwF+NvG118BLuAIhD7AO85azyP/MMldX99Jbw+s6n9pZ86+vhqvf+3gwccnHTfAtx/f\nx1PPHqAOzMzUeWH3NN/4zt5F7rmkcux80aPXbh7gB09P8vE//wl3f3OM373iNYv+ip2E/jpgV9Pj\n6Yjoy8ypFvvGgPXt2gC1zKwf8ty2hoeHFvQ7zu99ZGghzSWp63Tyu8NuoDk9exqB32rfENWpq12b\nmRbPlSQtkU5CfzuwFaAxP/9w075HgZMjYlNEDFBN7dw/R5tvRcSWxte/ANyz0AIkSZ2rzbfeTNOV\nOG+iem/lUuDNwNrMvLHp6p0eqqt3PtmqTWZ+NyL+EXATMEB1wvhgZrqYvSQtkXlDX5LUPbzQXJIK\nYuhLUkEMfUkqSFeuvTPf0hGvNBHxT4H/mplb2i1lEREfBP4N1XIYv5OZfx0RrwI+BxxNdV/E+zNz\nZFmK6EDjDu9twInAKuB3gO/QhfVGRC/VRQ1BVduHgAm6sNZZEXE08E3gfKpabqZ7a/1/VJeuA/wD\n8FFWSL3dOtI/uHQEcDXVMhCvSBHxG8AfA7O3B88uZfE2qiuj3hURxwD/jmr5i3cAvxsRq4BfAx5u\nPPfPgGuWuv8v0XuB5xv9/efAH9G99V4EkJlvpernR+neWmdP6P8T2NfY1M21DlLdiLql8edSVlC9\n3Rr6L1o6Ajhj7qevaN8Hfqnp8aFLWbwd+Hlge2ZOZuYuYAfV5bIH/x2anruS/SXwnxpf16hGP11Z\nb2b+b+DyxsMTqG5U7MpaG34f+DTw48bjbq71NGB1RHw1Iv6mca/Siqm3W0O/3TIQrziZ+VdA86e6\ntFrKopPlMOZd9mK5ZeaezByLiCHgf1GNcLq53qmI+Azwh8Dn6dJaI+JXgJHMvLNpc1fW2jBOdZJ7\nB9W03Yr63nZr6M+1dMQrXaulLDpZDuMVsexFRLwW+Brw2cz8Al1eb2a+H5i9afFVTbu6qdYPAOdH\nxN3A6VRTFkc37e+mWgEeAz6XmfXMfAx4HtjctH9Z6+3W0J9r6YhXulZLWXwDeFtEDEbEeqrVTx+h\n6d+BV8CyFxGxGfgqcFVmbmts7sp6I+JfR8RvNh6OU53c/r4ba83MczPzvMzcAjwIvA/4SjfW2vAB\nGu8jRsSrqUbuX10p9XblHbntloFY3l69fBFxInBLZp7ZbimLxlUAl1OdyD+WmX8VEauBzwDHAvuB\nSzLzmWUpogMR8Qng3UDz9+ojwB/QZfVGxBrgT4FjgH7g96jq68rv7azGaP9DVCe5rqy1sQ7ZzcDx\nVFfrXAU8xwqptytDX5LUWrdO70iSWjD0Jakghr4kFcTQl6SCGPqSVBBDX10vIrY0LhVcytc8MSJ+\nsJSvKXXC0Jekgrwi16ORXoafiYg7gOOArwNXABOZWYOD68NsycxfadU4It4EfCEz39h4/ItUN9X8\nEvAp4I1Ut9onL14gj4i4Gbg7M29uPK5nZi0i1gKfbLTtpVo++88Xr2TpcI70VYqTgA9T3aU9RHVX\naMcy8yGqhfve2Nj0y1Rrnp8N7G8s4/16qvVztrY+ymGuAb6ZmW8BzgV+KyJe91L6Jb1UjvRVir/L\nzO8BRMTngUtfxjE+C7wnIj4GbAF+NTMnIuL5iLgC+DngZGBth8d7O9USvB9oPF4DnAo8/jL6JnXE\nkb5K0bzKao3GctURUWts6+/gGF8A/iVwIXBnI/DfSbV07jjVWjp/1zh+s/rstsaHiczqBd6bmadn\n5unAmfx0HXXpiDD0VYpzIuL4xmJ87wfuoloE69RG8L9zvgNk5o+BJ4HfpJragWq0/heZ+afAM1TT\nNL2HNH2OagQP1ae6zfobqk9JIiKOBR6iWqRLOmIMfZXi21Sfv/sw8BTwJ1QfpfnXwP1Ub8B24rPA\nMHB34/FNwC9HxLeAW4EHqN4/aPYp4LyIeIjqo/Gebmz/z8CrIuIRqhPAb2Tm919yZdJL4CqbklQQ\n38iVmjTe5D21xa7bM/Pape6PtNgc6UtSQZzTl6SCGPqSVBBDX5IKYuhLUkEMfUkqyP8HhkLhLznJ\nGdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfad1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(flight['bu_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcc1db00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe843588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(hotel['bu_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe8db1d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HvlH+e0DYo2qm8zZdfYt+7tz9n8ys0vM8lfDGzbfQBOdumtqa4ryVfRL4e+BD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\nRCRBFPoiIgny/wET7I7Qv4S7cAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf1d8668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['bu_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf34a2b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VAV8DzgPOBi43s2Vx2WeAbwGNFXVdC3zB3c8kWm98rZktBz4JnAGcD3zZzBqmGtiBKndP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\nISIiif1/FOKKt6u3huoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf368e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(vacation['bu_value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 各BU的年化用户价值分布图形很相似，都是一个数量很高的头部，加上一个长尾，不容易观察，下图是取10的对数的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9Qv4i4FEU5RDwXeCbiqJ8VlGUr17Zif8V8C7wPlCtqurrC+euQBAZ07EPAPBX\n3TRtrHPIwTdfuAOTIcjnt1djkKPfrOm2tBEweOMSH9+241XMZi8njn2YgH96mKO5OBuAkubumHMl\npw6RU9DGhTPrcI9HLxkwESevi3LgaTIVIcsOBgaEkCcyMUMrqqqGgK9f9/jCpPEngSfj7JdAMCvk\n5iaMl+oJlJahZYZjvRM7bk2DnxzayMC4jU9uVme8+HM9TbZaADLGI98KnczaypkPDHt7iqjcsJ+B\n/mxqz898xb7pqpB3Ef1qUJjV66q53FZAzYkNVN0aOQe9IGniwLOTLZkzvw9JMmC372Z09C38/g5M\npti9TAXLD3EhSLAisD73DAD+bdN349WdmVR3uljt6ueWsjZd8zXbw0KerlPIZybErXc+hSRpvL/v\nc4RCM++bLmenhePkzV26Zl29rgaA04e3RrW7loIYOXMFwOG4HRBpiImMEHJB4qNpWJ5/Fs1oIrBx\n85Qhf1DmhTOrkaUQn9h8Mdpt/Sk02y5gCJlIdetvs3Y9eQV1ZGU3o9bupKNtTWT3J8XJkwdjH9jl\nF7XiTB3izJEthEKR31CePRkJiYsR+ndO4HCEG1cIIU9chJALEh7j2dPhsErlerBOjRu/e7GQvjE7\nt5W3kZsS+zARwCu56bA2kjZejKwrsWs6ZvM4q8pO4/XYOXzgkzHtJ+LkxfXtMW0lWWPTzlMMD6TS\nXBf5NwaLwUiRM5OLQx1R27pZrRswGtMYG9sv2r8lKELIBQmP5fnnAPBvrZryfNxn5LdqCUkWH/ev\ni3zx53pabXVoUkh3fHwmyipOYDT6OXLoYTzu5Jj2E3HyVfX6Qj+bdobz0M8e2RzVriIllyHfON2e\n4Yg2kmQgNfUO/P5WUdY2QZnbdkMgWC4Eg1he/A9CqakElbVThg5cKsATMPLRNfVTmkXEYiI+Plch\nT0vvICu7meGhTNAiH4ROZiJOXqJTyNduqUaSQlw4s46P8UJEu4qUPN5qO8vFwQ6ybZEbWKSm3kVv\n74uMjb2H2TyfcwHBUiB25IKExnToAIauy3g/+hAYr+1LRr0m9tUVYjf5dR9wTnAtY6V01v5IcpBy\n5RiaJnHxwk2AvqD81Th576CuOHlS8hgFpS3UV1fg90Xej61ODZcpiFZzBSAtLVxVcWxsvy5/BcsL\nIeSChMby/LMAeD8xtXrfnsPrGfOZua28FessduMALXaVZH86Nn/arP0pLKrGbh+hvVVhbDR9Vq+d\nTZwcYM3bN/tuAAAgAElEQVSmWvw+Mw0XIpfYdfsuA3Co8wxHOg9FtLPb12I0ZjE29p6IkycgQsgF\niYvHg+XVlwnm5ePfufvq42BI4tEDmzAbAty2unVWUw4bBhg09VDkVpB07qYnsFhHKSqpxuu10tQQ\nuQ56JGYTJ284k0taaniXfXjvTTScmblAWLYtCZNsiNotCECSJByO2wgEukWziQRECLkgYUn6m28h\nDw8RrFCw/uJnV5+/WVtC62Ay24q6cJgDUWaYyqFgA29awvFsbXz2BaTKK45jMARpqKsiGDTHfsF1\nXM5Ow2M1646TF5WGD3Cb6iPvyGVJJs+eTPvYMMEI9WL6+/fQ0fEYE0dmPT3/RH//ntk5L1hShJAL\nEhbjyXBZH/+Wqdkqjx9eD8DNs4yNAwxcKV2bPj67q/npGW1kutoYHMiiu6tk1uvClTh5Wb7uOLk9\naYys3A7amkoIBCJXFC1wpBDQQnS5o/cGnahP7vXWzc5xwZIjhFyQkEjDQxhrqwlm5xDKu3Zpp6kv\nmbfUErYXd1IQpcxrJAbsEzXIi3S/xmDwU15xHC0kUafqP+CciabyAkB/nLykvJ6A30xHS2R/rzVj\njt7SzWDIwGBIx+erR4tS7VGw/BBCLkhIzK+9ghQIENhSNaW58s8+qETTJL6861yUV0em39aC1Z+C\nLRC5vO31bNyyF5t9lLbWNYyP6X/dTDReEXK9+eTF5ZcAaL5UFtGm8MpV/bbR2HFys3k1mjZOIBD9\nWr9geSGEXJCQWCcuAW25Vm8kFIKnT6wlxerl4U2zDw94jMO4zQOzCqs4k3vZtPV1vB4bzY2zP+C8\nnsv5rlnFyfOLw79BtDfPf0cOk8MrF3WtL1geCCEXJBxy12VMB/YTLC5By7h2KPn+pQI6hpJ4aFPd\nrC4ATTCX1m47b34eo9HPpfqtBIOmWa95PbONkyenDpGcOkB7czGRsgZTzFaSjGbax2ML+UR9cp9P\nxMkTCSHkgoTD8tLzSKHQtEPOv39jJwDJVm/UphGRmO1BZ1Z2A2WrT9B9uZSeOR5wXo/c20tzbjh/\nfdWpCzGsw+QXtzA2kkxf18yZNpIkUeBIpds9ynjAG3UugyEFgyEbn+8SmuafnfOCJUMIuSDhsDz/\nLJrBQGDTlqvP3H4Dp9uySLO7Kc2MHguOxOwOOjV23vI8AB8c/iTzOeC8nsn1yfUwEV65VLM6ok1B\nUgoacGnocsz5LJbVaJoXt/ukrvUFS48QckFCYbhUh+n0Kfy334nmvNbD8DfVpXgDRqoKu5DnqKmz\nOegsXnWGvPw6Ghs20dVZMbcFIzDb+uQTQt5QGzmffCJOHuuqPlyLk4uytolDTCFXFEVWFOVRRVEO\nK4qyT1GUGT8tiqI8pijKP8bfRYHgGhOVDj3XXcl/7lRYTLcXx95xzsSwYWAWB50hbtr1EqGQxAcH\nPz6n9aIx2/rkeYWtSHKQS3qEfDB2NorFIoQ80dCzI38IsKqqugv4FvDI9QaKonwN2BBn3wSCqWga\nlheeQ7PZ8D3w4auPhz1m3laLyU0eJSdZX83x62m1hbM09IRVylafJCOzgzp1J4MDM1+Nny+zqbti\nMvvJzu2k6eIqAv6ZLwbl2/XvyGXZgdGYz/j4EUKhyI2bBcsHPUJ+C7AXQFXVI8CU9t2KouwGdgA/\njrt3AsEkjGdOYWy4hPdDD6AlTQqr1KzCGzCyuSB28+JItNnCWRrp4yVR7dZW7mP3rb9CC0kMDmSx\ntvI9lHXxrxg42/rk+cXNBPxmWhtm/o3CZjThsjp07chhcpz8qD6HBUuKnnrkycDkvKWgoihGVVUD\niqLkAv8DeBj49Iyvvo60NDtGY+TrxEuBy+WMbbSMSXT/Qcd7eOwxeDZc6dCal4P1xafBGe4G9Gq1\nAsCO0j6slrmlALY76sN++EswGiPvb7JzW3AkDXG5swyfLxn5ykdZNsTvsBOgOy89XJ/8UhtGU+zv\nS1FpKycOQUtdBWs3tsxs40zjRE8bXqOPTNv0ZhdO57XuSpK0nrGxfWjaEVyuD0+zXY4k+vdgPv7r\nEfJhYPIKsqqqE5WIPgVkAq8DOYBdUZQLqqo+EWmygYHxObq6MLhcTnp6YschlyuJ7j/oew/WoXEc\nR48h2e2MFpXBSPhX/mGPmb3VBazN7iPNOownenZdRJotKlZ/CiZPMgFmvp4uSSEKi88SCkk0N6wn\nFAwnbssG6er/jx8SzUVZKHXt2HsGGU6N/iXPKWgE4MLZMm7/yN4ZbXJt4TlOtjWyK0eZMuZ0WhkZ\nuRZGCYUKAQPd3W+RlPQX83gfi0Oifw/0+B9N6PWEVg4CDwIoirITuHr3WVXV76uqWqWq6h3APwK/\njCbiAsFcMdRdRB4Zxr9x85QGEntrSvAGjHO6yTnBROnaWAed5RXHcDiG6eosxeNZ+N3f1Tj5pdhx\n8sysHmyOsaiZK0VJ4fz0msHY88myFZttK273CYLBxBXIGwU9Qv4i4FEU5RDwXeCbiqJ8VlGUry6s\nawLBNUwnwrFa/7abpjx/6Ww4w2I+Qq7noFOSglTd9Gp4N960OOf6rQUuAAqaYmfiSLJG6dpLdLXl\nMjqUNKPNqitCXt0/c+jlehyO24EA4+OH9TksWDJihlZUVQ0BX7/u8bQrZ2InLlgopNERjOfOEsrI\nJFRccvX5sMfMWxeKWZvdh5I9wKHGvDnNP3HQGe1qfrlylNS0Ljray/F6ZhbKeNOZk07AIFPQHDvT\nBKBsbR3VxzfScKGcjTtOTxvPtDpIMdup7tfXbMPhuI3e3u8wNvYeTud9s/JdsLiI5suCZY/51ZeR\n/H58VduvVjrcc6SS4y3ZeANGijOG5nQlf4KJHfn1oZWrTZOlENt3vkIoJNPSuHhZtkGjga48Fznt\nvRgCAYLG6F/X0rXhA9tLtTMLuSRJVKYVcqhLZcg3TorZHnU+u30HkmQW+eQJgLjZKVj2WJ97BgB/\n1ZTMV063ZQGwZR5phwCttjqS/RkRb3RmZTVjt49wuaMMr9cxr7VmS1txDsZgkJz23pi2V4W8JnKc\nfH16IQA1OnblsmzDZtuBx3OGQKBfp8eCpUAIuWBZI7e1YjrwHoFVpVMqHXr8BmovZ5Azj0tAcO2g\ns9AdqU6JRmFxNZom0dq8bs7rzJX2ovCBZ0Fz7Dh5b3MSaZk91Fev5tLp3Bn7eK5PD58DnB/QH14B\njfHxg/qdFiw6QsgFyxrL888iaRqBqu1Tnld3ZhIIGeZ1CQiuhVUKPTMLeXpGO0nOQbq7ihclU+V6\n2opzAMjXIeQQroToGXfQ3+Oacbzyyo78vM44eVLS7QCMjcX/0pMgfgghFyxfNA3rc8+gWSz4N22e\nMnS8JSxwWwr0FZaKxMRBZ6F75sJXRSXVALQ2zT0GPx/6XWm4bRZdO3KA/KJwAa22ppkPbvPsaaRZ\nHJzXmblitW5Flh0iTr7MEUIuWLYYz5zCeFHFe/+HwXbtYK5nxMaFrnQK04bJSZ77BbNDwQbOWE8B\n0DVqmTaenNJNSmoPfb35jI2lzXmd+aDJEu1F2WT0DmIbc8e0Lyi50jGoZWYh/+DyYQrsTtrH+nmz\n+Z2Y88myGbt9F17vBfz++f2jKVg4hJALli2WZ58GwPvpz0x5/h+nKwhpMtuK5lbpcDLRStcWlZwH\noGWJduMQbjTR4Qpfpy84X4fcG/3QMzu/HYMhQHtz5FTK1Snhs4a64dgHqDCRTw7j42JXvlwRQi5Y\nnng8WJ9/llCmC98dd08ZeubEGmQpRFXh/IQ8Wo/OjMxWMjI7GBzIYngoa17rzJeO3AwAcjtjZ44Y\njUFyCtroas/D75u57szq5CtCPqRXyG8DYHRUCPlyRQi5YFmS9N//FHlgAP+GjVif/sXV52pXGqfa\nslmT3Y/TOr9WZNd6dE6/0bm56g0AWpuXbjc+QWdOOgC5l/WlAOYXtxAKGbjcnj/jeGlyBgZJ4uJQ\nT9R5+vv30N+/h/Hxk0iSjZGRV+jv3zM75wWLghBywbLEdPggmiTh37l7yvNnTqwBYHuxvtuO0RiI\n0Gw5OaWbstXHGB1Jo79vbrdF48lwsp0xu2UWQn4lTh4hvGIxGClOSqN5dAB3wBdzPkmSsVhWEwz2\nEwjo28ULFhch5IJlh6GmGmNTI8GKNVNyx0MhePakQrLVy/q8+QvKRI/O60Mrm7a8hSxrtDSvI569\nOOeMJNGZk07a4ChWd+zyjhNCHilzBcJx8qCmcbavWZcLZnO4WqLXq68htGBxEUIuWHbYfvY4AP5d\nN095frAhn9bBZD62sR6zYeZSs7MhfNCZPOWg02YfQll3kOGhTHq69bR9WxxmE15Jy+jD7hiNeuBZ\nkRLOMz/Z26BrfYslLOQ+n6rLXrC4CCEXLCukoUGsv3qaUEoqgbVTb1I+fSWs8pmt898VXuvRWTLl\n+YZN72A0Bjh98j7Qls/XozNXv5BLUnhXPjSQzlB/yow2Eweex3v0CbnBkInBkI7XW4emBXV6LVgs\nls8nVSAArE89iTQ+hv+W28BwrTPOuM/Ir8+upjB1mJtLY9fTjsVMpWtNZjeVG/cxPu5Erdkd6aVL\nQmdOOHMlR0fmClwLr0SqT55stpJvT+ZEzyW8wdiHxpIkYTYraJobt/uUTq8Fi4UQcsHyIRDA9viP\n0ex2fDt2Thl6vXoVI14z/6lKRY7Dp3am0rXr1r+HxeLm3Om7CQbN818kjgymOnBbzeTNInMFwpUQ\nI7E+LQdP0M9JnbvyifDK2Ni7uuwFi4cQcsGywfyb1zC0tuD59O+C/VqVwT1HKvnOb8O1VsyGwLxK\n1k5wfela2eBn4+a38fksVJ+7fd7zx50rB54Z/SNYdPSzm7iqH61jUGVauMzBoS59cW+LpQKQGB3d\np8tesHgIIRcsG+yP/QgA93/5gynPhz3mq1fys+dxJX8yLTYVmz/16kFnxZojOJKGqDl3O75FLlWr\nl8s54TIBOe3R878BrHYPGVldNFwoIxScOfNGSXVhkg0cvKxPyGXZgclUgNt9hFBo7hUnBfEnZmMJ\nRVFk4EfAJsALfEVV1fpJ458AvgVowFOqqn5vgXwVrFQeewx7zUVMHxwmsGYtpsNTS6aebM0mpMls\nL5p/7jjAoLGXIVMf+UObAFhbue9q4wi3O+laQ4llxtUbnq3dNJcVxLQvKG7mzLGb6GzJJ39V27Rx\ni8FIVWYpR7rr6HUPYyF2OMlsVvD7WxkbOyi6Bi0j9OzIHwKsqqruIizYj0wMKIpiINx0+R5gF/Bf\nFUXJnHEWgSAK5vfDZVJ9t94xbexYcw6yFGJrYXyKNrXYwjvQ9PFVAGS62rA7Rui6vAqfN3rXnKVk\nIgUxr01f6d6JA89ocfLdOeG494F2fZlA4fAKjI3t02UvWBz0CPktwF4AVVWPAFfbtKiqGgTWqqo6\nBGQABiD2VTGBYDKDgxjPnCKYlU2wQpkydOFyOm2DyazN6Zv3lfwJmu1h0QqnHk40jmBJGkfMhv50\nJ16ziVzdQh4+8IwWJ785J5zSub+9RtecZnMpkmRldFQceC4n9PTsTAaGJv05qCiKUVXVAICqqgFF\nUT4O/CvwGhA1eJaWZsdoNEQzWXRcrsVvGBBPEt1/9u1DCgYx3HsPzmTblKEX3g4fbO4u68FqmbkI\n1Gxpd4QPOl2+VRQW1ZGc0kdvTyFebyryHD+asmExboBKXM5No7ClB1sohD/GzyOvuAuT2Uejuhqr\nZeavulfrwGmy8HbzaR4uXsVtJbEOeq14vbcxMPAmycljWCw5c3wv8SfRvwfz8V+PkA8Dk1eQJ0R8\nAlVVX1AU5SXgCeALQMTKOgMD8Tmsihcul5OenpGldmPOJLr/0vAQmfv2EXIkMVa5GUY8V8dCIXjy\ncDlWY4AKVxce7/xvc4YI0WRRcXnzMfhsbNzyOhAuVRsKanOaUzZIc37tbOnMTqe4uZvM5su0ropV\nByZISUUD9TUVDA0asNhmznZZl5rNBz0tNAz0syXDM6PNZMzmW4E3aWl5hdTUz8S0XwwS/Xugx/9o\nQq8ntHIQeBBAUZSdwLmJAUVRkhVF2a8oikVV1RDh3fj8v22CGwbrE4+D243/9jvAPPWw7f1LBbQP\nOdlc0BWXK/kAveYO3IZRitwKGa4WCotrGBzIZmQ4MY52rl7V1xleKVtXjxaSaVDLItqsTw/3Ba0e\n0HcG4XDcCYg4+XJCj5C/CHgURTkEfBf4pqIon1UU5auqqg4DTwHvKYpygHDmyi+izCUQXGN8HPuj\nPwSbDd+uW6YNX6t0OP8GEhDuCPS25X0AQmMuNm8Nl6pdysYRs+XqVf1WfUK+en34YLfu/Myt7OBa\nPvn5AX0/Z6t1PQZDJqOj76Jpi/ObiCA6MUMrV3baX7/u8YVJ448Bj8XZL8ENgO2pn4U73jzwANim\nxsbHfUZ+fa6corRhSjMH47Zmv70RgDJjCmWrj9PbU8BA//Ru88uV3oxkfCaj7h35hJBfPLsmok26\nxU6+I5kLg934ggHMhuiyIEkySUl3MDT0H3i9KlZr5LkFi4O4ECRYGsbHsX3vn9HsDrjnnmnDr1WX\nMuo18+mtF5DjeI7Yb29C0mTu2nAaWdY4dfwBlkWpWp1osszlfBdZl/sx+gMx7ZPThskp7KC+uiLi\nxSCAjRl5+EJBjvdc0uXHtfCKyF5ZDgghFyw61p/vwfnHX8fQ3YVv125ISpoyvudIJd95O5zlajbG\nr9JeReU+huzNbLQlsUY5xthoCmbz8jp810NnQRaGUIisztg12RvO5JKT14Jn3M6RvVsj2m11hbsJ\nvdtxXpcPSUlhIRdpiMsDIeSCxcfrxfzO22hWK77b75w2HL6Sn0FR2hDZzvgJbTeDBKQgnyvSkGSN\npsaNJNJufIKOwnAP0TydcfKi0nA4qaWxNKLN2jQXNoOJd9urY8a9+/v3MDLyFgZDFmNj79LX9++i\nBdwSI4RcsOiYDh1AHhvFd+vtU4pjTXCiJRsNKW6HnBO0S33k26AqZ4TR0VR6u6f36kwEOgvCQq43\nTl5YGq5u2NqwKqKNUTawIT2HtrE+6of1/dwtFgVN8+L3N+myFywcQsgFi4vbjXn/O2gWy4zX8QGO\nt8T3Sv4E7VIvXygCWYLmxg0k4m4coCcnnYDBoFvI0zN7cSSN0NJQSrTN9uaMcF76u+36wisTZW29\nXtE1aKkRQi5YVGy/eAJ5dBTfLbeBfXpdk5rO8JX8dTl9JFnicyV/As3ezd3ZMDqSuLtxgJDBQFde\nJtkdfRgCsc8QJCm8Kx8ZSqX3siui3cb0XGRJ4h2dQm42lwOyEPJlgBByweLh9WL74ffQzGb8t90x\no8lE7vi2OIdVvJKbB4pHMEjQnKCx8cl0FGZhDAZxXe7TZV9cFg6vXDgTuZ5MkslCVWYZZ/qa6XYP\nRbSbQJZtmEzF+P0thEJufY4LFgQh5IJFw/r0LzB0duDfdQuaI2naeDAk8eypNdhMftbnxs7ImA2D\n2Ye4Oxs6Ry309hTGde7FRu7t5XJq+LeZvAv6uvsUl4crT6tn1ka1u69wExoab7ed1TVvuBpiCJ+v\nTpe9YGEQQi5YHPx+7D/4bsRMFYD3L+XTMZTE5oJuTHG6kj9B0bbnMEhwqnEVib4bh0klbXX28MzO\n7cRqH+PC6egVHu8r2AjAG61ndM1rsYR/g/J6598QWzB3hJALFgXrc89gaG3B/fkvoiUnz2hz9Up+\nUXzDKrbMOpTietQR8HRHvqqeSHRnpRKUJXJ09vCUZI3iskv0Xs6iJ0qcvHGohvLkDI521/Nm8zsx\n5zWZipEkG15vrbiuv4QIIRcsPIEA9n/5DprZjPuPvjGjyZjXyMvnyilOG2JVHK/kA+Ts+ncAnrhk\nJJ3ELnU6QdBooNuVSk7XgK4DT4CS8vCtTfV09PBKVWYBGhqn+tpjzilJBiyWNQSD/eLQcwkRQi5Y\ncCwvPY+hqRHPZz5PKC9/RptXzpeFr+RXqXG9kp9UcAJn0QmO9UPPgAtpBYRVJmgrcGEKBMnuiN3D\nE/THybdlhtvIHe+Z3h5uJiyWcLhmdPQtXfaC+COEXLCwhELh3bjRyPh/+2ZEs18eD4vL57bVxnPx\nq7vxxxohj4w4zr30tBaES+8WNerrZZqd24nDORo1cwXAZUuiOCmNmsEuhnyxb9ZOxMlHR9/U5Ycg\n/gghFywo5ld/jfGiiudTnyFUVDyjTXNfEvvrC9m9qp3SzNhpb3pJKd+PPauOmuZS6kchX1tpQh6O\ndRc2duiyl2QNZVMNPZ3ZUePkEN6VBzVN1+UggyEZk6mQ8fFDBIOJ29whkRFCLlg4QiEc//x/0GSZ\n8T/5s4hmPz+yGk2T+Nz2+O3GJdlPzs6fEgoaebY+BVh5Qj6YmsSow0phk74dOcC6rdUA1JxYH9Vu\nuyucovlmm97slXVomp+xsf26fRHEDyHkggXD8h+/wlhzHu/HP0WodOYONT89XMn331mP2RBk1Gtk\nz5H4NHlIr3wNS0onfec/wgkaSfW5cDL9JmlCI0m0FrhIGRwlZWBY10sqq8L54eePbYxql2N3UuBI\n4UDnBUb9sdu/WSzh0JiIky8NQsgFC8P4OI7//XdoRiOBijVYf77n6n+TaehNpXfUxqb8bqym+JSs\nlU1jZG/7BUGfnZqzdzFqHKR0PPoONFG5Fl7RtyvPKewkI7uHmlPro9Ynh3D2ii8UYH9HTcx5TaZi\nDIY0RkbeFGmIS0BMIVcURVYU5VFFUQ4rirJPUZTy68Z/V1GUDxRFOXjFTvzjIMD+43/F0NGO77Y7\n0NLTI9p90BzuzrOjRH94IBZZVU9jtA/Sc/LTqHIzgBDyK0gSVFadY2zYSXN95GqIcC17RU94Jdw1\n6G4CgXa83ngeWAv0oEd0HwKsqqruAr4FPDIxoCiKDfh74E5VVW8GUoCPLISjgsTBcKkO+/ceIZSZ\nie+ueyPajXpNnGrNIsPhocw1EJe1Tc7LZG56Ht+Ii54zn6TBEY4Jr1Qh78wNV0Is0nngCWEhh9jh\nlQJHCiXOLN7rqMEd8MWcNynpPkBkrywFeoT8FmAvgKqqR4Btk8a8wG5VVSdylIxA7ICaYOXi9+P8\nw68ijY8z+u3vgNUa0fTlc2X4gkZ2lnbFLXc8d+fjyEY/l498BS1gpdF+HmvQTq6nJD4LLDOCRgPt\nxdnktPdgcXtj2jecycVh6QEpxPH9VTScidyvVJIk7ivYiDvo48Dl2LvspKR7AImRESHki03M5stA\nMjA5JyyoKIpRVdXAlcbMXQCKovwxkAREPe1IS7NjNBrm6u+C4HIl9m2/ZeX/Rz8KJ0/Ajh0kh7zg\njCzkvzoVPtjctaoLq8U076WtWedJrXgX/0gOFquXjsof0y21Ua7lMVL5C+QFvAwkG5buolGzUkRx\nQwelzZ3UbZj5UHkyyWke8grbaWtcRTBox2oJy8DE/07mYeUmHqt9m3c6z/PxtTuizpubu4qOju2M\njh4hLS2E0Zgytzc0R5bV92AOzMd/PUI+DFPuNcuqql7t+nolJv7/AxXAJ1RVjXrSMTCwvHokulxO\nenoSN/d1OflvfvsNkl97DS01jbGPPAQjkX85a+hN4V01n7LMAVxODx7vfGuPa+Tf9AMABupuwx8I\n0aR1gwEKQ5mEFvAATjZIhIJLd8DXsCqf24BCtYXaNSW6XlO2poaOlkIuni9n9dZLWC1GPN7pzZzd\nnnoyrQ72Np3i9QtvcWv+rRHn7OkZwWq9i5GRozQ3v0Jy8sfm+I5mz3L6HswFPf5HE3o9oZWDwIMA\niqLsBM5dN/5jwAo8NCnEIrhBmMhEsT3yTyT//hfAYMT9e18GW/RUvz1HwjHr3aWx63noIaV8H46c\nCwzW34ZvKNzpplkKdxgqIisuayxXWktyCRgMrKrTd6UeQNkQvuijnot+diBJEtsyC3AH/VQPxO7Y\nNBEnHxkRaYiLiZ4d+YvAvYqiHCJc//NLiqJ8lnAY5TjwZeB94B1FUQC+p6rqiwvkr2A54vdh+/lP\nkTxu3L/7eUIF0et9e/wGnjy6jgyHm8353ej7GE4nfd2r4f8jB8jZ8TO0kAF3z7XQQqN0GaNmoECL\nfosx0QmYjbSV5FDU0I513IPHHjmcNUFuQRvJqQPU1awjGIy+n9uWWcDeNpUTvbH/obDZtmIwZDA6\n+gaaFkKSRBLbYhDzG3QlDv716x5PLj4s/qZucCwvv4ShsxPfrpsJVG2Paf/S2XL6x218447jGA3z\nD0k4C05itI4w0lxF0BOOyw4xTpc0yKpQDiaW15nMQtBUXkDJpXaKGjq4uL40pr0kQcX6ao4fuIWL\n59awZcfFiLalyRmkmm2c7G3HHwpikiP/PCVJxum8n8HBp3C7T2C3x/48COaPEGHBvDCeP4v58EGC\nubl4f+chXa95/PAGJEnjS7v09YaMhmwaw1l8jKDPxnDzTVef19IKQKmWM+81ljtyby9NV+Knpefq\nkHv1dVeqqAz//E8d3BbVTpYkqjLzGQv4ONoduRNQf/8e+vv3IEk2ALq7v01//56I9oL4IYRcMHfc\nbiwvPY9mMOD53BfBZI75klNtLj5oyuPuimZWZei7Vh6N5NLDyEY/w4270IKWq8+rpbCQr7oBhBzC\nJW0DBplVTfqbcpSsrsds8XDqYBWxzoKv1l7R0TnIYlGQJDMez/XHaYKFQgi5YM7YH/0h8uAgvlvv\nIJQTOR95Mn/6wh0AlLsG5l1XxeTowZFbjX80g7HOqYd2NbRi0UzkEflW6UoiaDTQUphFTtcAjlF9\njZCNxiAVldX0dGZzqTZ6OKYiJZNkk4W3284SDEVvwydJZszmNQSDXQQCsQ9IBfNHCLlgTshdl7F/\n758JOZLw3XOfrtd0DDk41ZpNTvIoa7L1tSiLjEbK6veQJI3BS7eCdu2j3M0Q3dIQJVo28g30EW8o\nDf9jOptd+fqqkwAc2HtzVDtZktmamU+fd5QTvbEbPlutGwDErnyRuHE+5YK4Yv/Hv0caH8N3/4NR\nb08qaw0AACAASURBVG9O5icHNxLSZO5Y3Yo0z/szKWXvYU1rxd27Cm9/yZSxszQBUKbp+y1hpXBp\nVTiMVNqgv25N2ZoLOFOHOPT2TgKB6IfC2zL1h1es1kpAxuM5q9sXwdwRQi6YNYZzZ7H+8kkCa9bi\nv2mnrteMeY08fngDSRYfVfNsriwZ3eTe/ChayMBg3e3Txs9ITQBUaDO3lVupXM5JZ8xuoayxk5hB\n7ysYDCF23HmI4cEUqo9viGq7JjWLFLOdt9rOENKih1dk2YHZXIbf34zfr78OjGBuCCEXzA5NI+l/\n/BWSpjH6P/83GPSl9j19Yi2Dbis3l7ZhNkQXgVhkb/slZmcPIy1VBD2pU8Z8BKihlXwtg1SS5rVO\nwiFJNJbkkDzixtWlP3S1+94DABx6K/KtTQCjLHNX/nq63EOc6WuOOa/VugmA4eGXdPsimBtCyAWz\nwvzGbzAfeA/vPffhv/NuXa8JheBH72/GbAhyS9n8bnKaU9rI3PwcvpEsRpqn5yjX0opPCrCZknmt\nk6hcuhInL1NbdL9m1ZpL5BR2cvLAdsb+b3tnHh9Vdff/9509k31PSEIgEA8G2VVkj1RbRYSCUtFa\nt7bW/rSLtmrbxz4/beuvtk+17WNd6gZaa63a0rqjVhGFsC8SlgMECIGQfU9mv/f3xwQFss1MgDDk\nvHnNi5k75577ncm9nzn3LJ9va2yvZS/LGw/A2wc39VlvUMg1mpvV+sBTjRJyReh4vcTe/18YZjPt\n9z8Y8m7Ldw1jb20yX5u4iwRH33aoPWMwZMZjmMx+Kj+9DUPvarS1pbNbZZzRu9f22cq+4Z1Cvqvv\nFvNR9n+WzZhJG/F5bby+pHcX6qlZo0iyxfJ2+Sb8eu+JQMzmBGy2kbhca/H5QrcPUISPEnJFyMQs\neRrLvjLcN95C4BzRZ/kla0azZM1o7ntjOgA5Sf0zNUoYVkJC/npaKybSsq9rN8BevZYN7MVuWDHr\nfc9pPxtpSYylOj2J4XsrsHpDNyKbeNF6TGY/G1dP6bV7fWP1WiamZVHvaWPpzn/3Wa/DMQGA5mbV\nvXIqUUKuCAmtoR7nw79BT0ik/e6fhbxfdauTPbUpFKY3MCSxPfLjW9xkT38cI2Cm8pPboRtL2ioa\nadLaKTRyMA/iU3vvyCFYfQGGhWGiFRvfTtG4rdRVZ7H7s1G9lp2SkQ9ASU2o/eRmWlr+EXIsivAZ\nvGe7IixiH3wguPhnZjH2t17vNv9md6zeF3QinNZPl8OsC5/HnlhF7dar8DTmd1tmlym4mvNco3fT\nrrOd3SODs3XO2bk/rP0mTVsNwIev95zVCWBEQhqpdicb6w7R4e89mYXZHEds7Excro14vQfCikcR\nOkrIFX1iWbeWmL8sJZCVjW9G1+l+PeELmFhXPoQ4u5cxObURHz9z8rOkjX8Nf0civtZ0Uore/ML5\n8Bh2ahWYDRMjjSERH+ts4FBuOi6HjcLt+0OehggwtGAf6VlVbFg5mca65B7LmTSNqZnDcAf8vHtw\nS5/1JiZeDUBz86shx6IIDyXkit7x+Yi/504APFd9LeTphgBbD6fT4bUyOb8SiylCl0OTn+RR76Np\nBo3ykm4HOCHYrVKjNTHCyMZO/7MNRTO62UTZqHySG1vDmoaoaXDhzJUE/JY+W+UzswvQgFf3lfRZ\nb0LCPDTNQVPTSxinMMHHYEYJuaJXnH/4HZYdpbiuv5HA8L7tUY9l9b7gLf6UgsgXhGSe/yK2uDra\nKs/D09Rzl8kGygA41xga8bHOJnYXBWftnLM9vO6VsedvIDahlRWvX4LX0/MPYrojltHJWWyu28+e\n5t5XkprNiSQkzMXrLcPlWhdWPIrQUEKu6BHngw8EBziTkvCL3gfATmRPTRJldckUpjeQHheaiVOX\n42eVkjHpJfyueJr39r5YZZ22G5OhIYzciI51trGnaBi6pnHuZ3vD2s9q83Hx3P/Q2pxAyQfTey07\nKzv4w/5qWd+t8qSk6wBoavpbWPEoQkMJuaJ7Ojpw/O1FNF3Hfc11faZuO5EX1gWdDadG2Bo3WdvJ\nu+QhABp2XnacRe2JVNHIfq2GEUY2TnouN5joiHNyYGQueeVVJDSGN+3zS19djtnsZ/mrV6DrPZvi\nTEjNId2RwLL962jz9ZyftaFhCR5POSZTIk1Nf6O+/inlU36S6VPIhRAmIcSTQogSIcQKIcTIbso4\nhRCrhAiz2aY4Y4n72d2Ya6rxTp9JoLDvOePH4vWb+Ov6c3HafIwZEskgp0Huxb/HnlhFzabFeJt7\n90xZQzC7zXnGsAiOdfayY1zwUi0Ks1WenN7IRZesorI8l02f9px0wmIyce3I6bT6XCzb33uXiaaZ\niIk5H8Nw4Xb3P6GI4nhCaZF/FXBIKacAPwEePvZNIcT5wEpgRDf7KqIQ+99fIualvxDIzcMzN/xM\n6O/sGE5du5MLhh7BGoGvStq4f5BUuIL2yvOoXn9Dr2UNDEo0idUwq26VYzDV1bErNwUDKNqwI+z9\n5173LzRN540XF/Q68WVkvBWLZuLpHctZXflpr3XGxAQzOHV0rAo7HkXvhCLk04F3AaSUa4ATf6Lt\nwAKOz+OpiFIsn20h/p470eMTcH3jJrCEnxh56dpgkocpw8PvVonN2UL21KfwtadSvvznoPd+/Arq\nqNQaGM9wHAzO1Zw90R4XQ/nQDIZW1BLf3Bbyfvu2ZuNqhHPHbaV8TwHvvXxxj2UTbA6mZuZT425j\nc33vf2+rNQubbSRe7x58vv45YCqOJ5SrNAFoPuZ1QAhhkVL6AaSUqwCECO32OznZicVyZiXDTe/M\ndxitnLT4KyvhxmvB7Ub799+JOxK6r/VRyuvj+HD3UKYUVDE8wwshTgV02K3Ykg6Qd9kDgMaR//wC\nSyALix2svZwv6/RgDslppnMxdbPa83RhMg/csXtjV1E+ww7WcN62MtZfPLHXshbr8d9z8ZwP2bFl\nAp+8dxnzbvykRw/5eQWj+aRqP29V7OTeKXa0Xs3mv8SRI3vx+UpO+nU3mK/jUIS8BTj2CKajIh4J\njY0dke56SkhPj6e2tn8eIAPJyYrf8efHcT75J8yHD+O+Yh6+3eFNWzvKEx+NwzA0rj+/FLcnNHFz\n2K34LUcY9pUfY3a0cvCDe2iqEEDQK8Tp796caa9ey8fm7diwEOuPRWdg5iibzBp64MycH106aiiX\nvreBcSWllEwf12M5i9WM33f895yWcYhzx21l59ZxfPr+RC6Y1X0/eJo1lgvS81hXW8HrcgOzc3r2\nNTcMgcmUREtLCVVVhzGbEyL7YCcwGK7j3oQ+lK6VVcAcACHERYDK3XS20d5OzHNPYT58CO/kKfiK\nZ0dUTUDXeHFdEfF2LwvH7Q55P5O9ieFzf4otvpbmfVMxmb2fr97sbgXnUQ5RR5PWzigjD2tIbZLB\nR3tcDLsLc8k+XEvWoZqw9599xVtopgCvPXNtrxmE5uePRgMe3fZOr0knNM2M0zkVw/DQ1PRy2PEo\nuicUIV8GuIUQq4HfA3cKIa4TQtx6akNTnBba20m84Vos+/fhGz8huHozwjxsH8ihHG6OZ9EESaw9\ntJs2s6OZ3Dk/IiZtP22HxnXrMd4TpaagaZOardI7W8YF5yFMXLs97H1TM2qZNKWE6kPZrHyr5x/4\nnNhEJmcMZWfT4T5nsDidUwAzDQ2PYRgR39wrjqHPZoyUUgduO2Fzl4FNKWXxSYpJcZrQWltIvG4R\n1rUl+EaPwX3tN8AU+dKCX7wzFYBkp4sla0b3Wd4S08jweT/BkVpG3bYrcdcV0J2rYXcE0NmuHSDG\nsFFgZEUc82Bgz8ghtCY4GbNxF+/Nm4HfGt7dy8zLllO6aRL/fG4RFxaXEJfY/cDpouHj+Kyhmt9u\n+TezhhSR5ui+28RsTsDpnExHx2qam1/5fLGQInLUgqBBitbcROLXvop1bQnu+Qtx33BzWD4qJ7Kv\nLpHSyjTyklvIS+67r9KWUMmIq35ATFoZTTvmU7nye4Qq4gCb2Ueb5uY8YxgWzqzB8zMNw2RiywVF\nODs8FG3ZE/b+cfFtzL/pNdpaEnjl6Wt7LJfqcHLX2Lk0ezv41cZ/9OqrEht7KZpmpbb2f1Sr/CSg\nhHwQojU1krhoPtaNG3AvWkzrk8/2S8QBnvh0HAYaFxce7LNnJiZzJ4XX3IY9sZKWA5Px1I8gpejt\nsI73gRbM5H6Bfk6kIQ8qNkwdQ8CkMe2jjWE5Ih7l0oXvkltQzsq3vsSe0p6/82tHTmdC2nDerdjC\nX/as7LGcxZJCUtL1eL1lNDe/FnY8iuNRQj7I0BobSLx6PtYtm/FdMBnfBZNx/PWFftXZ5LLx4roi\nkmLcjM/tfUAt+dx3GLHgLkxWF427i2nZP4VwWuIAlTSwXatgmJ5JBkl976CgKTWR7ePPIauyLqw0\ncEc5uD2DS+YGMwI9+cvb2bWue0/49dVruGHkeSRYHTy0aRnranpeVZqWdhdgobb2N+h6f1IAKpSQ\nDyK0+noSr5qH9bMteCdPwb1ocb/6xI/y/JrzaPfamDmyAnNPdrUmP0NmPEre7IfRfXbqPptP++Hx\nER3v89a4oVrjoWKqq6NkYnDQc/ryNZjq6sKuY2jBfibPWkF9TSbLl321x3LJdie3F01F0+CHq5Zw\npL2x23I2Wz4pKTfj9ZZRX/9o2PEovkAJ+SDA8cISYh5/lOTZU7GWfoZ3yrTg7JSTIOIun5k/rZxA\nnN3b40pOS0wjBfPvJm3sv3HVD2Pvq4/jaRgW0fEaaGMFpaQa8Ywa5JmAwqUqK4Wy4VkUHKgityKy\nRB9fuvJNsnIPsXnNFNZ+OKXHciIpnetGTKDB08b3Vj2LJ9B9/tCMjJ9jNqdTW/tblUGoHyghHwRo\nLS3EPPknzEeO4J06A8/CRSdFxAGWrjmP6tZYvjNtK05b10GrITMeRXz9RuKGbKOjppCG7XOIy90U\n8fFe19bh0wIsMC4a1Hk5I+XjGWMB+MoHG9H08PvKLZYAC7/xAlabh2d+8132bOv5rmj2kJEsHD6Z\n0oYK7t/wSreDn2ZzEllZD2IYLo4cuUclnogQdSWc5ZjL9uB89PeYq47gnT4Tz4KrIp4nfiJun5k/\nfDSJWJuXO2ZtPu49k9VFTvEjpI39Nyarh6a9M2jYPgcjELkfygb9AB9RSooRT7ae3t/wByUVQzMo\nLcon93Ad522SEdWRllnLopuXEvCb+cN/3UNlefep9TRN47LcPIbHJ7Ns/zoe3Nj9WExi4jXExs6k\nre1dGhuXRhTTYEcJ+VmM9cP3SbriUkyNDXi+fDme+QtPmogDPL92NEda4vj21M9Ijf3Cj9qZvY3C\na24ldfTbeNvSqNmwmLaKSYQ7qHkiy00b0TWdYn2sao33gw9mT8BvNnHpG59id/eePLknRp67i5vv\nfor21jgeuvO/qSjrPjOTzWTmjqLpxFvt/K1sMxtqyrqU0TSNIUMex2xOoarqx3R0qCxC4aKuhrMQ\nra2V2PvuJWnxVWitrbgXLcb75ctOqog3dth56P3JxNm9fL842Bo3xzSSU/wwIxbchS2hipqN11Cz\nYTG+9v63njdRxi5TBflGBmPUSs5+0ZwUx6dTR5PY3MbcVz6MaDoiQE72buYsepWWxiQe/P7/Zd/O\n7p2sUx3BwU/DgB+seo6qjqYuZWy2oeTmPodhBKiouAGfL3zDtsGMEvKzCY8Hx5JnSLlwPM6nnsA/\nspCmdz/EN7nnQalIuf75OdS3xzD7nHLe3JHHG66djPr6TaSOfgdPQz5lyx6has23wei/B4oLDy9o\nKzAZGlcELkQbQJfDs4VPpo+hIj+LsZsk49eF71d+lPOnrWb+dX/F44rhtz+6D7m1+9wyo5IyuHbE\neOo9bdy28imavV3N8+LiZpOZeT9+fyUHDszB5zsUcVyDDSXkZwNuN47nniblognE33sXWksznsvm\n4LrlViybIx9Y7InSylQ+LcslI66NeV9+mcLFtzJk+pNoJj+Nu4up2zYPR3J5r4ZXoWJgsET7kHqt\nlWnGaDVv/CShm028dsPluBw2rnjtI/L2Ri6a4y7cwNU3PY/PZ+Xhe3/KlpLu7XIvySnk2pHT2NV0\nmO98/GfafF2X+qem/oC0tB/h9Zaxf/9leDxdu2IUXdFO9yhxbW3rGTUsHdX2ly4X6f96mcCvH8Jc\ndQQjJgbXjd9Ez8jESDg59qAn4vaZ+fJjC0koWMUPb7uP5GyJoZtorzyPlgNT0H0xYddptZjx9WBV\nu5LtPG16nxFGFtcFZp+RfeNnso1tX4zce5jFr6zAZ7Ww9I5FHMnLiLiuVlcmj91/Jz6fletuf55L\nFy7vUkY3DJ7etZaSmnLy4vN5f9EKUhypXcrV1v4PNTW/xGRKICfnMRISes9UFdXXMSHb2PZ4K6qE\nPBpPgPZ2Yp5/Dudjf8RUW4Nhs+GdOgPfrIsx4k+dub7X2szSQ43kTPwnWVnlGIZG055iajZcT2x2\n5O7GPQn5ZxzgEe117Fj4pfF1WvXu5yIPNNEs5ACjtx/gqmWf4o6x88pNV7BPdD9wGQqHy4fy8jPf\npL01gQkXreG79z+GzX783y1g6Dwn17Oq+gAjEjJ5tvj/kOXseqfV0bGOlpZXMQwvyck3kZn5AGZz\ncrfHjcrr+BiUkPeTaDoBtLZW4u+4DevHH2Jqb8ew29Fmz6btoukYsXGn5JgBk5vG5K00pGygJX4P\nmmbg9cTQJC+lcdsCPE3BRTn96UbpTsi3Uc4j2usAfF2/mOFnsMNhtAs5wNjt+7ny9TVohs5782ey\ndsZ4DFNkYxFNDcm88twtVB3KJa+gnFt/9hh5Iw4eV0Y3DF7Zt5V3D0mGOJN5pvi7FCRkdqnL56ui\nqWkpfv8RzOY0srJ+SWLitWja8Xdm0XQdd4cS8n4SDSeAqboKx5JniFnyNKbGRgxHDN4Zs/DOmEl8\nZiqtre6+KwkRA50O52Gqst7D7ajBY6/FMAVFtrR0Cqs/uYpvXbibyhbnSTvmsUKuo/MmG3hNK0FD\nY7E+k3PO8KTKZ4OQm8wa2Y0eFj/3BnFtLg4U5PDmotnUZnft9ggFv8/C8mUL2Lh6KiZTgK8seou5\n1/+L2LgvBjkNw6C02cXDW98gwRrD76bcwMwhRV3qMgw/7e0raG19F/BhsWQTHz8Xu300qam3ANFx\nHfeGEvJ+csaeAIaBZfNG4n56N5atm9F0HcPpxDuzGO+0mRAT7IuOj3dELOQGBl5bA66Yw3Q4D9Hh\nPExb3D4Cli8uNrMniRUfL+CJpffR0ZLGvd94gryMk5s412ox4/X72UEFL2krOajVkWLEsSAwjTzO\n/IU/Z4uQ6wGD2DYXc95dT9Gug+ga7BhXyOqLJ3F4aGZE01f37hzF269dTVN9KnaHiy9f/Q7FV/6H\n1Iz6z8usqj7A0t0b8OsBbjhnFt8bM4c4q6NLXYFAA62t7+ByrQcMLJZcsrJ+RULCPDIyks/M6zhE\nlJD3kzNKyNvbsa5bg+3jj7C//QbmA8G8mYHMLHwzZuGbeD7Yjl8Z2ZeQGxj4LW147HWdj1o89jrc\njjo89hoCFtdx5W2eFOJbC/EHzKxaczmPvHwL+6uGkJlSy48WP012WmQeHT3RiotN5n18oG/lgBZ0\nThyvF3CpPpFYul7MZyJnk5AfpXDPIYpX7WBIZ3q46uxUNk8ezdbzR9ERF97dmM9rZd3KGZSsKKaj\nLR5N0xk9aRvjp25izIVbyMyp5kBrA0/sLKHa1UaqPY6rCi7iivyJjEzIxnyCnYTPV0lb23u43VsA\nA6s1l5yc72CzXY3VmtPv72IgOOVCLoQwAY8D4wAP8C0p5d5j3r8S+G/ADzwnpXy6t/oGpZAbBlpL\nM6baWkw11Zhqa9DqajHV1eGtbqLuiB//vkMEDhzGb5jxY0Fz2AlcNBktLRmGDcNk0jFbPFgd7Vjt\nbZhs7VgdbTgT/LR5WwiY3QTMLgJmFz5rS+ejGZ+1Bd3cjUWoYcLijyW+dQSOjlx8DQVUlp/HzvIR\nrN4/hA/2ZNPQmoim6XzlwpVcPftt7Nb+Dza68bGPKrZrFezgIPuoRtcMNENjlJHHdH00OUR2Oz9Q\nnI1CDoBhMGLfESZs2csoeQizrhMwmdhfmMvu0QXsL8ylNjM15L50r8dG6aYJbF5zEYfLh32+PWNI\nFUWTtpFXWIY3YxdvuV+gjaBjYqzFztjUfMam5nNO4hBGJmYxPD4Dm9mC31+D319Fc/Pf0fXgVEan\n8yLi4+cSGzsTh2MMmhYdSUdOh5AvBOZJKW/qTL78Uynl/M73rMBO4AKgnWCi5rlSyuqe6uuPkAcC\nTei6B9ABHcPQu3luYBg6Wksjht8Hhg8MP4YRCP6v+4EAhu7HMPzEx1lpaWkD/GAEQA8mjjUMPbji\nzTDQdD+ax4vhdaN5PBguDx1tOoZuoOugaxqGoYHbhdHugo4OfO1e3O06rg4dtwtaA7E0aYk0kUQz\nibSY43E7rBCj43C0ExPT9vnj6Guns5XY2ObORwsWS+iZVAxDw92eiLs9mfaWDFoas2msy6W2ehhH\njhRQVZVPfUsytc2J1LckEtCPP+HjnC1cMLaEqZM+Ji21Gh3juH+d33Tn8y+2B9Bx4aUNF224acNN\nq+ammkYO00C99sXJqhkaOaRSxFCKAvkkERvpqTGgnLVCfgwxHW7GlB5g7M4Kciq+uLxdMXZqs1Jo\nSEuiIzYGl9OBy2nH47ARMJvxW8wEzCYCFjMB8xfPa9sz2FE+lh37x7K3fBSuji/+9iZTgPj0Q5BQ\niSfuIG7nAYipB2sHWF1oNjfJsSZS481kJQ4jOz6GC/J3kJ+wnrSYXWha8HPohhM/I9C1AtAyQEtF\nM6WBKQ2IDYq8ZkHDgoYVTbOAKZbYuFjs9mAsRxefaZ3dSj295sT3uyln1kwkOSKfddNfIX8EWCel\nfLnz9WEpZU7n87HAb6WUl3W+/j2wWkr5ak/1RSrkra3vcPDgYiC6L5hQMXQNr9eJuyMer9eB2x2L\n2xWPyxWPqyOejo542tqSaGlJoaE5lZaWVOobM2hrS6KhIYvGxgx0vfdVlTarh6S4VhJjW4mJa6A0\n5SP0tJ2QWwLpO/trjdKFOCOGdCORTJIYbmSRb2TgwBb1Qhjt8UN4nyG+pZ2RZZUMPVhLbmUdKQ2t\nmPrRRevDwjbGsIXxbGYCm5nAHgqpoesslr5ITq5i0qQPmDDhI4qK1pCXJzGbu1+j0B2BgJnv/fhN\nds65CmxdV5/2l59ceB93nX9Pl+39FfJQ1k8nAM3HvA4IISxSSn8377UCiZEG0/t+X6Og4GuR7Kro\nEXvnIw0YDkwa2HAUgxIrMLHz0X+ygOs7H5GxYzMEOxhOL+npka8BCWWZXAtw7BFMnSLe3XvxQFdH\nHIVCoVCcMkIR8lXAHIDOPvJjl/DtBAqFEClCCBswEyg56VEqFAqFokfCmbUylmCv6c0E74LipJRP\nHTNrxURw1spjpzZkhUKhUBzLaZ9HrlAoFIqTy5lnJadQKBSKsFBCrlAoFFGOEnKFQqGIcvqfhyuK\nEUIkAi8SnA9vA+6SUkblrBshxAJgkZTyuoGOJRT6sn6IFoQQk4HfSCmLBzqWcOlcmf0cMIzggoJf\nSSlfH9CgwkQIYQaeBgTB1YK3SSlLBzaq8BFCZAAbgUullLvC3X+wt8jvAv4jpZwF3ARE5YwbIcQf\ngV8TXX/PrwIOKeUU4CfAwwMcT9gIIe4BnoEocffqyvVAvZRyBnAZ8KcBjicSrgSQUk4D7gMeHNhw\nwqfzB/XPgKuvsj0RTRf+qeD3BL9ACN6dnDxj79PLauC7Ax1EmEwH3gWQUq4Bzh/YcCKiDFg40EH0\ng1eBn3c+1wga30UVUsp/Abd2vswnOhck/g54EqiMtIJB07UihPgmcOcJm2+WUq4XQmQR7GL54emP\nLHR6+Qx/F0IUD0BI/aE364eoQEr5DyHEsIGOI1KklG0AQoh44DWCLdqoQ0rpF0I8DywArh7oeMJB\nCHETUCulXC6E+Gmk9QwaIZdSPgs8e+J2IcQY4GXgx1LKj097YGHQ02eIUnqzflCcJoQQecAy4HEp\n5UsDHU+kSClvFELcC6wVQhRJKU+/WUpk3AIYQohLgPHAC0KIeVLKsLK3DBoh7w4hRBHB28trpJRb\nBzqeQcYqgv2br3Rj/aA4DQghMoH3gDuklP8Z6HgiQQjxDSBXSvlroIOjvtZRgpRy5tHnQogVBAdr\nw07BNaiFnOAAoQP4oxACoPmo17rilLMMuFQIsZovrB8Up5efAcnAz4UQR/vKL5dSRjzoNgD8E1gi\nhFhJ0Ejxh1EW/0lBLdFXKBSKKGewz1pRKBSKqEcJuUKhUEQ5SsgVCoUiylFCrlAoFFGOEnKFQqGI\ncpSQK6IOIURx55zbgTr+iihcSas4i1FCrlAoFFHOYF8QpIhihBDnAE8BKUA78P1O75xc4K8EF7ts\nA2ZJKXN7qWcTcKuUckOnLWo5wby0s4AfATGdj29JKVces18xcP9RC1shxFJghZRyqRDiBoLePSaC\n9qS3Symj1ZRNcYajWuSKaOZF4H+llGMJmom9JoSwA38E/t65/TUgp496/gIs7nw+G/gMqANuA+ZK\nKccBDwF3hxKUEGI08G1gqpRyPFAD/DicD6ZQhIMSckW0EgeMlFL+Ez63wm0gmGDgUoLijJRyGX1b\nm/4NWCiE0IBrgRellDpBN72vCCF+QdCvPi7E2C4GCoE1QogtwHxgVOgfTaEIDyXkimjFRNCj5Vg0\ngt2FAcI4tztNinYDxcAlwL+EEHHAemA4sBL4326OZ5ywzdr5vxl4RUo5vrNFfiFwR6jxKBThooRc\nEa20AGVCiIUAnQ6KWUAp8D5wXef2y4GkEOr7C8EsRSuklB3AOQRd9P4f8CFwOUGBPpY6oEAI4RBC\npAAzOrevABYIITI6W/lPcIZ73SuiGyXkimjmeuD7QohtBNOULZRSegmK5lVCiM3ANYSWNWYZuHJZ\nLAAAAJ5JREFUwe6QFztfbwW2ALuATUAbwQw0nyOl3A68BWwnaIf8Sef2rcADBH8AthO8zh6K+FMq\nFH2g3A8VZx1CiO8DH0gpdwghJgJPSyknDXRcCsWpQgm54qyjszvlIYJdI27gdoKt7W5TaXX2YysU\nUYsScoVCoYhyVB+5QqFQRDlKyBUKhSLKUUKuUCgUUY4ScoVCoYhylJArFApFlPP/ARqdI3hMM4Le\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf5650b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sns.distplot(train['log_value'],color='r')\n",
    "ax2=sns.distplot(flight['log_value'],color='g')\n",
    "ax3=sns.distplot(hotel['log_value'],color='b')\n",
    "ax4=sns.distplot(vacation['log_value'],color='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
